AI/ML Based Network Security App

The AI/ML-Based Network Security Application presented in this study provides an intelligent, multi-layered defense system for detecting and mitigating modern cyber threats. By combining traditional network monitoring tools with artificial intelligence (AI) and machine learning (ML) models, the proposed framework achieves proactive and adaptive threat detection. The system integrates Python libraries such as Scapy, python-nmap, and Scikit-learn to perform real-time packet inspection, vulnerability scanning, and phishing classification. Trained ML algorithms, including Random Forest and Logistic Regression, are employed to enhance the accuracy of phishing and anomaly detection tasks. A modular architecture with multi-threaded execution ensures concurrent processing of security functions without compromising graphical responsiveness. Experimental evaluation demonstrates the system’s efficiency in identifying phishing attempts, scanning vulnerabilities, and analyzing live network packets. The proposed model provides a scalable and intelligent platform for automated network protection, bridging the gap between conventional rule-based defenses and adaptive AI-driven security systems.